Li BW, Ma XY, Lai S, Sun X, Sun MJ, Chang B. Development and validation of a prognostic nomogram for colorectal cancer after surgery. World J Clin Cases 2021; 9(21): 5860-5872 [PMID: 34368305 DOI: 10.12998/wjcc.v9.i21.5860]
Corresponding Author of This Article
Bing Chang, MD, Assistant Professor, Department of Gastroenterology, The Frist Hospital of China Medical University, No. 155 Nanjing North Street, Shenyang 110000, Liaoning Province, China. cb000216@163.com
Research Domain of This Article
Gastroenterology & Hepatology
Article-Type of This Article
Retrospective Study
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
Bo-Wen Li, Shuang Lai, Xin Sun, Bing Chang, Department of Gastroenterology, The Frist Hospital of China Medical University, Shenyang 110000, Liaoning Province, China
Xiao-Yu Ma, Ming-Jun Sun, Department of Gastroenterology and Endoscopy, The Frist Hospital of China Medical University, Shenyang 110000, Liaoning Province, China
Author contributions: Li BW performed the research and wrote the paper; Ma XY supervised the report; Lai S and Sun X contributed to the analysis; Sun MJ and Chang B proposed the idea and clinical advice.
Supported byScience and Technology Support Program of Shenyang, No. 20-205-4-094.
Institutional review board statement: The experimental data are from the Surveillance, Epidemiology, and End Results (SEER) database, not clinical cases from any medical institutions. Therefore, our research does not need to be approved by an ethics committee.
Informed consent statement: The experimental data are from the Surveillance, Epidemiology, and End Results (SEER) database, not clinical cases from any medical institutions. Because the data is anonymous and the patients’ personal privacy information is not available, informed consent is not required.
Conflict-of-interest statement: The authors have no conflict of interest to declare.
Data sharing statement: No additional data are available.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Bing Chang, MD, Assistant Professor, Department of Gastroenterology, The Frist Hospital of China Medical University, No. 155 Nanjing North Street, Shenyang 110000, Liaoning Province, China. cb000216@163.com
Received: February 25, 2021 Peer-review started: February 25, 2021 First decision: May 1, 2021 Revised: May 17, 2021 Accepted: May 25, 2021 Article in press: May 25, 2021 Published online: July 26, 2021 Processing time: 146 Days and 5.7 Hours
ARTICLE HIGHLIGHTS
Research background
A nomogram is an effective tool to predict patient outcomes intuitively. Lymph node (LN) metastasis and tumor deposit (TD) conditions affect the prognosis of patients with colorectal cancer (CRC) after surgery markedly. At present, establishing an effective tool to predict the overall survival (OS) of CRC patients after surgery is necessary.
Research motivation
To establish a predictive model to assess the prognosis of CRC patients after surgery.
Research objectives
To screen out the suitable risk factors that can affect the OS of CRC patients after surgery and establish a nomogram with these factors.
Research methods
A total of 3139 patients diagnosed with CRC after surgery from the Surveillance, Epidemiology, and End Results program were divided into a training set (n = 2029) and a validation set (n = 1047) randomly. The Harrell concordance index (C-index), Akaike information criterion, and area under the curve (AUC) were used to assess the predictive efficacy of the N stage from the American Joint Committee Cancer tumor-node-metastasis classification, LN ratio, and log odds of positive lymph nodes (LODDS). Construction of the nomogram was based on the risk factors screened out through univariate and multivariate analyses. The C-index, receiver operating characteristic (ROC) curve, calibration curve, and likelihood ratio test were used to assess the final nomogram.
Research results
Seven independent predictive factors, namely, race, age at diagnosis, T stage, M stage, LODDS, TD condition, and serum carcinoembryonic antigen level, were included in the nomogram. The C-index of the nomogram for OS prediction was 0.8002 (95%CI: 0.7839-0.8165) in the training set and 0.7864 (95%CI: 0.7604-0.8124) in the validation set. The AUC values of the ROC curve predicting the 1-, 3-, and 5-year OS were 0.846, 0.841, and 0.825, respectively, in the training set and 0.823, 0.817, and 0.835, respectively, in the validation test. The final nomogram showed better sensitivity and specificity than the nomogram with N stage alone for evaluating LN metastasis in both the training set (-4668.0 vs -4688.3, P < 0.001) and the validation set (-1919.5 vs -1919.8, P < 0.001) through the likelihood ratio test.
Research conclusions
The nomogram incorporating LODDS, TD, and other risk factors shows a great predictive accuracy and better sensitivity and specificity and represents a potential tool for therapeutic decision-making.
Research perspectives
Suitable combination of LODDS and TD is necessary to simplify the nomogram. Larger sample size studies are required to include more potential risk factors, improve the nomogram, and stratify the prognosis further.
